Title: k-Means Clustering
Slug: k-means_clustering
Summary: How to conduct k-means clustering in scikit-learn.
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Clustering
Authors: Chris Albon

Preliminaries



In [1]:

    
# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans

Load Iris Flower Dataset



In [2]:

    
# Load data
iris = datasets.load_iris()
X = iris.data

Standardize Features



In [3]:

    
# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Conduct k-Means Clustering



In [4]:

    
# Create k-mean object
clt = KMeans(n_clusters=3, random_state=0, n_jobs=-1)

# Train model
model = clt.fit(X_std)

Show Each Observation's Cluster Membership



In [5]:

    
# View predict class
model.labels_









    Out[5]:





array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2,
       2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2,
       0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0,
       2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2], dtype=int32)

Create New Observation



In [6]:

    
# Create new observation
new_observation = [[0.8, 0.8, 0.8, 0.8]]

Predict Observation's Cluster



In [7]:

    
# Predict observation's cluster
model.predict(new_observation)









    Out[7]:





array([0], dtype=int32)

View Centers Of Each Cluster



In [8]:

    
# View cluster centers
model.cluster_centers_









    Out[8]:





array([[ 1.13597027,  0.09659843,  0.996271  ,  1.01717187],
       [-1.01457897,  0.84230679, -1.30487835, -1.25512862],
       [-0.05021989, -0.88029181,  0.34753171,  0.28206327]])